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Articles by Xiaohan Nie
M-LLM based video frame selection for efficient video understanding
Recent advances in Multi-Modal Large Language Models (M-LLMs) show promising results in video reasoning. Popular Multi-Modal Large Language Model (M-LLM) frameworks usually apply naive uniform sampling to reduce the number of video frames that are fed into an M-LLM, particularly for long context videos. However, it could lose crucial context in certain periods of a video, so that the downstream M-LLM may not have sufficient visual information to answer a question.
A robust and efficient framework for sports-field registration
We propose a novel framework to register sports-fields as they appear in broadcast sports videos. Unlike previous approaches, we particularly address the challenge of field registration when: (a) there are not enough distinguishable features on the field, and (b) no prior knowledge is available about the camera.
Depth-guided sparse structure-from-motion for movies and TV shows
Existing approaches for Structure from Motion (SfM) produce impressive 3-D reconstruction results especially when using imagery captured with large parallax. However, to create engaging video-content in movies and TV shows, the amount by which a camera can be moved while filming a particular shot is often limited. The resulting small-motion parallax between video frames makes standard geometry-based SfM approaches not as effective for movies and TV shows.
Shot contrastive self-supervised learning for scene boundary detection
Scenes play a crucial role in breaking the storyline of movies and TV episodes into semantically cohesive parts. However, given their complex temporal structure, finding scene boundaries can be a challenging task requiring large amounts of labeled training data. To address this challenge, we present a self-supervised shot contrastive learning approach (ShotCoL) to learn a shot representation that maximizes the similarity between nearby shots compared to randomly selected shots.
Automatically identifying scene boundaries in movies and TV shows
Scene boundary detection is the problem of localizing where scenes in a video begin and end. It’s an important step towards semantic understanding of video, with applications in scene classification, video retrieval and search, and video summarization, among other things. In a paper we presented at this year’s Conference on Computer Vision and Pattern Recognition (CVPR), we described ShotCoL, a new self-supervised algorithm for scene boundary detection.
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